August 2019 - talks - lists.cs.princeton.edu

Marcela Melara will present her FPO, "Intra-Process Least Privilege and Isolation for Emerging Applications" on Thursday, 9/12/2019 at 1:30pm, in CS 402
by Nicki Mahler 29 Aug '19

29 Aug '19

Marcela Melara will present her FPO, " Intra-Process Least Privilege and Isolation for Emerging Applications" on Thursday, 9/12/2019 at 1:30pm in CS 402. The members of her committee are: Michael Freedman (adviser), Readers: Amit Levy and Mic Bowman (Intel Labs); Examiners: Wyatt Lloyd and Prateek Mittal (ELE) A copy of her thesis is available upon request. Everyone is invited to attend her talk. The abstract follows below: Third-party libraries reduce software development costs and e↵ort. Designed for flexible reuse, libraries implement a small set of features, allowing developers to build applications by combining libraries that provide the desired functionality. However, third-party code also poses a great risk: because the source code is rarely inspected or even accessible by the application developer, bugs or vulnerabilities that can leak sensitive data may go unnoticed. Yet, existing data protection tools are insucient because they do not enforce least privilege, restricting each library’s access to only those data it needs for its functionality. Prior academic proposals have addressed this issue with two main approaches: (1) running application components in separate processes for strong isolation, or (2) tracking individual data objects throughout the application to prevent unprivileged components from disclosing sensitive information. However, these approaches see limited real-world adoption because they introduce significant development overhead and integration complexity. This dissertation proposes intra-process least privilege, a design principle that facilitates enforcing least privilege for application developers by restricting access at the granularity of individual library functions, and strongly isolating data within a single process address space. We first present Pyronia, a privilege separation system for language runtimes that targets IoT device applications. To protect sensitive OS resources, Pyronia combines three access control techniques: system call interposition, stack inspection, and page table replication. Developers then specify data access rules only for directly imported third- party functions in a central policy. We next present Grin, a memory access control system for Intel SGX cloud applications. Intel SGX enables developers to run sensitive code inside an enclave, a hardware-protected memory region within an applications address space. However, in practice, developers often include untrusted third-party libraries in the enclave, giving them unfettered access to all inenclave data. Grin leverages Memory Protection Keys (MPK) to partition an enclave and assign per-compartment access rules. Developers declare sensitive data objects and access privileges for in-enclave functions. Grin then automatically confines these data objects in MPK compartments. Pyronia and Grin demonstrate the e↵ectiveness of our intra-process least privilege approach in today’s privacy-critical applications while easing integration e↵orts for developers

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Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402
by Nicki Mahler 21 Aug '19

21 Aug '19

Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402. The members of his committee are as follows: Mona Singh (Adviser); Examiners: Ben Raphael and Bernard Chazelle; Readers: Olga Troyanskaya and Barbara Engelhardt. A copy of his thesis, is available upon request. Everyone is invited to attend his talk. The abstract follows below: Despite the incredible influx of sequencing data pinpointing the gene variants responsible for the development of heterogeneous diseases remains a particularly hard task because the same phenotypic outcome (disease) can result from a myriad of combinations of different alterations across the genome. A promising avenue is to consider genome alterations within the context of pathways because different alterations within any of several genes comprising the same pathway can have similar consequences with respect to disease development. Large-scale biological networks provide a helpful proxy for biological pathway knowledge as genes that participate in the same pathway tend to interact with each other and form modules within the larger network. In this dissertation, I introduce two novel methods that further our ability to computationally highlight potential disease-causing genes by examining disease genomes in the context of biological networks. First, in Chapter 2, I present a novel network-based approach which tackles cancer mutational heterogeneity by utilizing per-individual mutational profiles. I provide an intuitive formulation relying on balancing the size of a connected subgraph within the larger network with covering many patients. I describe a machine learning-like schema for selecting the value of the single required parameter and both an integer linear programming framework and a fast heuristic for optimizing the objective function. I demonstrate the outstanding performance of my method in identifying cancer-relevant genes, especially those mutated at very low rates. Next, in Chapter 3, I propose a general computational framework that uses prior knowledge of disease-associated genes to guide a network-based search for novel ones based upon newly acquired information. I use a graph di↵usion kernel to spread the signal from the set of already known disease genes and then use it to bias a random walk originating from the newly implicated genes to move closer to the known ones. I demonstrate that integrating the two types of information is better than using either one of them alone. I show, in the context of cancer, that my method readily outperforms other network-based methods. Finally, I apply my approach to several complex diseases, thereby demonstrating its versatility in a broad range of settings.

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IEEE SSCS Distinguished Lecture Colloquium at Princeton - August 22, 2019 - E-Quad B205 - Prof. Zhihua Wang, Tsinghua University
by Emily Lawrence 20 Aug '19

20 Aug '19

Sent on behalf of Prof. Kaushik Sengupta **Refreshments at 1:00 pm and a chance to talk with speaker ** IEEE Princeton/Central Jersey Section - Distinguished Lecture Colloquium, August 2019 Speaker: Prof. Zhihua Wang, Tsinghua University Talk Title: Binaural Hearing Aid System and the Intelligent Acoustic Signal Processing Date: Thursday, August 22, 2019 Time: 1:30 - 2:30 p.m. Location: Princeton University, Department of Electrical Engineering, Engineering Quadrangle, Room B205 Abstract: Nowadays, the hearing aid technology is facing a new horizon based on advanced digital signal processing, wireless communication and artificial intelligence. In this lecture, new methodology with a systematic solution covering both the auditory periphery and the cognitive system is given. The up to date and rapidly evolves technologies for the binaural hearing aid system are well addressed. The multi-channel wide dynamic range compression, active noise reduction, self-adaptable directivity, acoustic scene analysis, and the wireless linking with other audio or communication systems are presented. The key technologies including the ultra-low power chip design, the advanced digital signal processing (DSP), and the wireless system integration and connectivity are discussed. The micromechanics and high performance electro-acoustics, the miniaturized antenna, the user interface, and the fitting system development are also the important aspects for the hearing aid technologies are shown in this lecture. A smart binaural hearing aid technology, which simultaneously processes the acoustic signals from the four microphones in both ears are indicated which utilizing the computing power from the smart phone to finish the advanced binaural DSP algorithms. Bio: Zhihua Wang (M'99-SM'04-F'17) received the B.S., M.S., and Ph.D. degrees in Electronic Engineering in 1983, 1985 and 1990, respectively, from Tsinghua University, Beijing, China, where he has served as full professor and Deputy Director of the Institute of Microelectronics since 1997 and 2000. He was a visiting scholar at CMU (1992-1993) and KU Leuven (1993-1994), and was a visiting professor at HKUST (2014.9-2015.3). His current research mainly focuses on CMOS RFIC and biomedical applications, involving RFID, PLL, low-power wireless transceivers, and smart clinic equipment combined with leading edge RFIC and digital image processing techniques. He has co-authored 12 books/chapters, over 183 (480) papers in international journals (conferences), over 244 (29) papers in Chinese journals (conferences) and holds 123 Chinese and 8 US patents. Prof. Wang has served as the chairman of IEEE SSCS Beijing Chapter (1999-2009), an AdCom Member of the IEEE SSCS (2016-2019), a technology program committee member of the IEEE ISSCC (2005-2011), a steering committee member of the IEEE A-SSCC (2005-), the technical program chair for A-SSCC 2013, a guest editor for IEEE JSSC Special Issues (2006.12, 2009.12 and 2014.11), an associate editor of IEEE Trans on CAS-I, CAS-II and IEEE Trans on BioCAS, and other administrative/expert committee positions in China's national science and technology projects. Contact Info: Nagi Naganathan, <mailto:nagisub@gmail.com> nagisub(a)gmail.com or Kaushik Sengupta, <mailto:kaushiks@princeton.edu> kaushiks(a)princeton.edu

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Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006
by Nicki Mahler 01 Aug '19

01 Aug '19

Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006. The members of his committee are as follows: Karthik Narasimhan (advisor), Sanjeev Arora, and Olga Russakovsky. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Recent advances in machine learning for NLP have shown that there are great benefits to unsupervised pre-training followed by finetuning to specific tasks (BERT, GPT): in particular, BERT was able to obtain state of the art results on 11 NLP tasks using this method. However, having these models perform well on domains that are different from the one where they were trained is still a challenge. In our work, we explore methods to transfer representations learnt by BERT to different models for improved performance on NLP tasks in a different domain-- medical papers. We generate a dataset from medical reviews obtainable on the Cochrane Database of Systematic Reviews, to train models to learn representations on technical documents that match representations learnt by BERT on plain text summaries of those documents. We experiment with different methods to perform section-wise matching between documents to create our parallel corpus, both for performing the matching and transferring representations. Performance on the domain transfer task is reported on both intrinsic and extrinsic evaluation metrics including named entity recognition on the EBM-NLP and sentence similarity on the MED-STS datasets. Reading list: 1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016. 2. Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 3. Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke: Deep contextualized word representations, Proc. of NAACL, 2018. 4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I: Attention is all you need. In Advances in neural information processing systems, 2017. 5. Jiaqi Mu, Suma Bhat, Pramod Viswanath: All-but-the-Top: Simple and effective Postprocessing for Word Representations, International Conference on Learning Representations, 2018. 6. Tan, Chuanqi & Sun, Fuchun & Kong, Tao & Zhang, Wenchang & Yang, Chao & Liu, Chunfang: A Survey on Deep Transfer Learning: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III. 10.1007/978-3-030-01424-7_27. 7. Sinno Jialin Pan and Qiang Yang and Wei Fan and Sinno Jialin Pan: A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 2010. 8. Yifan Peng and Shankai Yan and Zhiyong Lu, Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets, Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019), 2019. 9.Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J. Marshall, Ani Nenkova, Byron C. Wallace: A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018. 10. Yanshan Wang, Naveed Afzal, Sunyang Fu, Liwei Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang Liu: MedSTS: a resource for clinical semantic textual similarity, Language Resources and Evaluation, 2018.

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